def example_use_descrs():
print("Example usage of descriptors...")
patch_size = 16
image = imageio.imread('images/Lenna.png')
image = image / 255.
x_coord = 26
y_coord = 473
patch = image[x_coord:x_coord + patch_size,
y_coord:y_coord + patch_size, :]
encoder = init_descr_32(patch_size=patch_size)
patch_descr1 = compute_descriptor(patch, encoder)
print("patch descr shape", patch_descr1.shape)
print("patch descr:")
print_descr_3dims(patch_descr1)
encoder_IR, encoder_mp = init_IR(image.shape[0], image.shape[1],
patch_size)
image_IR = compute_IR(image, encoder_IR)
print("patch IR")
print_descr_3dims(image_IR[x_coord:x_coord + patch_size,
y_coord:y_coord + patch_size, :])
patch_descr2 = compute_descriptor_from_IR(image_IR, x_coord, y_coord,
patch_size, encoder_mp)
print("patch descr shape", patch_descr2.shape)
print("patch descr from IR:")
print_descr_3dims(patch_descr2)
print("diff")
print(patch_descr1 - patch_descr2)
print("print descr")
print_descr_3dims(patch_descr1 - patch_descr2)
def main():
x_queries = [118]
y_queries = [351]
image_path = '/home/niaki/Downloads/baboon.png' #'/home/niaki/Downloads/barbara.bmp'
patch_size = 16
patch_width = patch_size
patch_height = patch_size
nr_similar_patches = 5
query_stride = 100
compare_stride = 3
eps = 0.0001
image = imageio.imread(image_path)
image_height = image.shape[0]
image_width = image.shape[1]
image = image / 255.
encoder32 = init_descr_32(16)
encoder128 = init_descr(
model_version='16_alex_layer1finetuned_2_finetuned_3conv3mp_lamb',
nr_feature_maps_layer1=32,
nr_feature_maps_layer23=32,
patch_height=patch_height,
patch_width=patch_width)
mask_path = "/home/niaki/Downloads/mask_16"
with open(mask_path, 'rb') as f:
mask = pickle.load(f)
results_patches_x_coords_0, results_patches_y_coords_0 = retrieve_patches_for_queries_and_descr(
x_queries,
y_queries,
1,
image_path,
mask,
encoder32,
encoder128,
patch_size=patch_size,
compare_stride=compare_stride,
nr_similar_patches=nr_similar_patches)
results_patches_x_coords_1, results_patches_y_coords_1 = retrieve_patches_for_queries_and_descr(
x_queries,
y_queries,
2,
image_path,
mask,
encoder32,
encoder128,
patch_size=patch_size,
compare_stride=compare_stride,
nr_similar_patches=nr_similar_patches)
results_patches_x_coords_2, results_patches_y_coords_2 = retrieve_patches_for_queries_and_descr(
x_queries,
y_queries,
3,
image_path,
mask,
encoder32,
encoder128,
patch_size=patch_size,
compare_stride=compare_stride,
nr_similar_patches=nr_similar_patches)
pickle_vars_for_visualisation(
x_queries, y_queries, results_patches_x_coords_0,
results_patches_y_coords_0, results_patches_x_coords_1,
results_patches_y_coords_1, results_patches_x_coords_2,
results_patches_y_coords_2, image_path, mask_path, patch_size,
nr_similar_patches)
##### (comment EITHER: everything above here in main(), OR the unpickling line just bellow
# x_queries, y_queries, results_patches_x_coords_0, results_patches_y_coords_0, results_patches_x_coords_1, \
# results_patches_y_coords_1, results_patches_x_coords_2, results_patches_y_coords_2, image_path, mask_path, \
# patch_size, nr_similar_patches = \
# unpickle_vars("../zimnica/visualisation_445_88_missing21_20191215_140006.pickle")
with open(mask_path, 'rb') as f:
mask = pickle.load(f)
generate_visualisation_for_3_descrs(x_queries,
y_queries,
results_patches_x_coords_0,
results_patches_y_coords_0,
results_patches_x_coords_1,
results_patches_y_coords_1,
results_patches_x_coords_2,
results_patches_y_coords_2,
image_path,
mask,
patch_size=patch_size,
nr_similar_patches=nr_similar_patches)
import matplotlib
image_path = '/home/niaki/PycharmProjects/patch-desc-ae/images/Lenna.png'
patch_size = 16
nr_similar_patches = 40
query_stride = 32
compare_stride = 8
eps = 0.0001
results_dir = '/home/niaki/PycharmProjects/patch-desc-ae/results'
results_version = '4'
image = imageio.imread(image_path)
image_height = image.shape[0]
image_width = image.shape[1]
encoder32 = init_descr_32(16)
encoder128 = init_descr_128(16)
total_nr_query_patches = len(
range(0, image_width - patch_size + 1, query_stride)) * len(
range(0, image_height - patch_size + 1, query_stride))
#TODO add function calculate_ssd/calculate_psnr as a parameter to this function
def calculate_SSDs_for_desc_and_noise_level(which_desc, noise_level):
image_noisy = add_gaussian_noise(image, sigma=noise_level)
query_x_coords = []
query_y_coords = []
def main():
x_queries = [124]
y_queries = [359]
image_path = '/home/niaki/Downloads/monarch_cropped_mirrored.png'
patch_size = 16
patch_width = patch_size
patch_height = patch_size
nr_similar_patches = 5
query_stride = 100
compare_stride = 2
eps = 0.0001
image = imageio.imread(image_path)
image_height = image.shape[0]
image_width = image.shape[1]
image = image / 255.
encoder32 = init_descr_32(16)
encoder128 = init_descr(
model_version='16_alex_layer1finetuned_2_finetuned_3conv3mp_lamb',
nr_feature_maps_layer1=32,
nr_feature_maps_layer23=32,
patch_height=patch_height,
patch_width=patch_width)
random_seed = 124
noise_level = 0
results_patches_x_coords_0, results_patches_y_coords_0 = retrieve_patches_for_queries_and_descr(
x_queries,
y_queries,
1,
image_path,
random_seed,
noise_level,
encoder32,
encoder128,
patch_size=patch_size,
compare_stride=compare_stride,
nr_similar_patches=nr_similar_patches)
results_patches_x_coords_1, results_patches_y_coords_1 = retrieve_patches_for_queries_and_descr(
x_queries,
y_queries,
2,
image_path,
random_seed,
noise_level,
encoder32,
encoder128,
patch_size=patch_size,
compare_stride=compare_stride,
nr_similar_patches=nr_similar_patches)
results_patches_x_coords_2, results_patches_y_coords_2 = retrieve_patches_for_queries_and_descr(
x_queries,
y_queries,
3,
image_path,
random_seed,
noise_level,
encoder32,
encoder128,
patch_size=patch_size,
compare_stride=compare_stride,
nr_similar_patches=nr_similar_patches)
pickle_vars_for_visualisation(
x_queries, y_queries, results_patches_x_coords_0,
results_patches_y_coords_0, results_patches_x_coords_1,
results_patches_y_coords_1, results_patches_x_coords_2,
results_patches_y_coords_2, image_path, random_seed, noise_level,
patch_size, nr_similar_patches)
##### (comment EITHER: everything above here in main(), OR the unpickling line just bellow
# x_queries, y_queries, results_patches_x_coords_0, results_patches_y_coords_0, results_patches_x_coords_1, \
# results_patches_y_coords_1, results_patches_x_coords_2, results_patches_y_coords_2, image_path, random_seed, noise_level, \
# patch_size, nr_similar_patches = \
# unpickle_vars("../zimnica/somethingsomething.pickle")
generate_visualisation_for_3_descrs(x_queries,
y_queries,
results_patches_x_coords_0,
results_patches_y_coords_0,
results_patches_x_coords_1,
results_patches_y_coords_1,
results_patches_x_coords_2,
results_patches_y_coords_2,
image_path,
random_seed,
noise_level,
patch_size=patch_size,
nr_similar_patches=nr_similar_patches)